Diabetes mellitus affects over 25 million individuals in the United States and is a leading cause of morbidity and mortality. At least 1% of diabetes (>250,000 individuals) results from high penetrant single gene defects, in HNF1A, GCK and HNF4A and several other genes. Unfortunately, as a result of phenotypic overlap with more common forms of diabetes, lack of awareness and/or techniques for identifying them among health care providers, and the cost and labor historically involved in sequencing several genes as required to make a diagnosis, the vast majority of cases of highly penetrant and genetic diabetes are misdiagnosed as type 1 (T1DM) or type 2 diabetes (T2DM). Published evidence shows that diagnosing highly penetrant genetic forms of diabetes enables personalized treatment resulting in improved glucose control, better prediction of prognosis, and an enhanced familial risk assessment. To meet this need, we propose to implement and evaluate in four diverse health care settings our Personalized Diabetes Medicine Program (PDMP). The PDMP is currently based at the University of Maryland Center for Diabetes and Endocrinology and will be disseminated to UM Family Medicine and three partner centers: the Baltimore Veterans Administration Medical Center (BVAMC, with opportunities to disseminate nationally), Geisinger Medical Center (an integrated health system) and Bay West Endocrinology Associates (a community-based private practice group). We will also engage the community through advertisements and site visits to local primary care practices by our genetic counselor/diabetes educator team. The PDMP consists of: a simple patient screening questionnaire, chart/electronic health record (EHR) review, an algorithm that includes utilizes questionnaire data, routine lab testing, and family history review to identify patients most likely to have highl penetrant genetic diabetes; customized multiplex gene panel sequencing of eligible patients followed by confirmation of diabetes-causal mutations in our CLIA-approved Translational Genomics Laboratory; incorporation of mutations and decision support in the EHR; genetic counseling; implementing a mutation-based treatment strategy; and family screening. Deliverables include EHR-based implementation tools, the sequencing panel and contribution of genotype/phenotype data regarding diabetes-causal variants and variants of unknown clinical significance to ClinVar and other similar public resources. We will track implementation metrics of the PDMP and conduct an impact evaluation, including evaluation of clinical outcomes as measured by changes in glycemic control in patients diagnosed with a genetic form of diabetes. Finally, we will engage a Payer Advisory Panel in the development of the impact evaluation process to enhance our ability to collect meaningful evidence to inform clinical practice recommendations and guide insurance coverage decisions as a first step to enabling diagnosis of inherited forms of diabetes across the United States and more broadly, genomic diagnosis and treatment of highly penetrant genetic forms of other common diseases.